Abstract
Micro RNAs (miRNAs) are small non coding RNA sequences consisting of 20–23 nucleotides that govern the post transcriptional expression of genes in both normal and disease condition of the cell. Thus, identification of most influencing miRNAs and the associated mRNAs becomes a research quest in diagnostic and prognostic application of cancer. In this study we conducted an integrated analysis of Next Generation Sequencing based miRNA-mRNA expression data to identify dysregulated miRNAs and their target mRNAs for Oral Cancer. A sensible combination of datamining tools such as Random Forest (RF), K-nearest Neighbour (KNN), Support Vector Machine (SVM), log-Fold Change, Adjusted p-values, Matthews coefficient correlation (MCC), Prediction accuracy was considered for this analysis. The prioritized cancer specific target genes obtained in this approach exhibited a MCC value of 0.9 and achieved a consistently higher prediction accuracy of 95% when subjected to classifiers RF, KNN and SVM. These target genes can be presented as predictive variables for early diagnosis of cancer. The selected miRNA-target genes can further be biologically validated to confirm their participation in disease specific pathways and biological processes.
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Mahapatra, S., Prasath, R., Swarnkar, T. (2022). Integrative Analysis of miRNA-mRNA Expression Data to Identify miRNA-Targets for Oral Cancer. In: Chbeir, R., Manolopoulos, Y., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2021. Lecture Notes in Computer Science(), vol 13119. Springer, Cham. https://doi.org/10.1007/978-3-031-21517-9_3
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